In this section, we describe the object detector and the domain adaptation strategies we employed in this research. We exploited Faster R-CNN [1]}, a widely used state-of-the-art object detector that we briefly review in sec:virtual-to-real:method:FasterRCNN and that it is also described more in detail in sec:back:cnn-based-detectors:two-stages-detectors.
We trained this cnn using viped, our collection of synthetic images automatically annotated, already outlined in the previous section. To mitigate the existing domain shift between these data and the real-world ones, we proposed two supervised da techniques (for a better explanation of da, see also sec:back:domainadaptation). The first one, described in sec:virtual-to-real:method:domainadaptation1, consists of training the detector with the synthetic data and then fine-tuning it by exploiting the real-world images. In the second approach, described in sec:virtual-to-real:method:domainadaptation2, we employed instead another supervised technique, called Balanced Gradient Contribution (BGC) [2]}, [3]}, where we mixed the synthetic and the real-world data during the training phase. finetuningarch and mixedbatcharch show an overview of the two solutions.
